Download Machine Learning and Data Mining

Machine Learning and Data Mining
Benno Stein
ML:1
Introduction
Theo Lettmann
© STEIN/LETTMANN 2005-2015
Contents
I. Introduction
II. Machine Learning Basics
III. Decision Trees
IV. Statistical Learning
V. Linear Models
VI. Neural Networks
VII. Kernel Methods
VIII. Learning Theory
IX. Combined Models and Meta Learning
X. Latent Variables Analysis
XI. Cluster Analysis
XII. Other Unsupervised Learning Methods
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Introduction
© STEIN/LETTMANN 2005-2015
Objectives
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q
understand and explain the basic concepts of machine learning
q
understand formalized concepts and methods and be able to implement
them in the form of algorithms
q
sensibly select, adapt, and apply relevant methods
q
being able to educate oneself
Introduction
© STEIN/LETTMANN 2005-2015
Related Fields
1. Statistics
[paradigms, models]
2. Mathematics
3. Information Retrieval
[methods, algorithms]
4. Knowledge Processing
5. Heuristic Search
6. Decision Support Systems
[applications]
7. Business Intelligence
8. Web Technology
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Introduction
© STEIN/LETTMANN 2005-2015
Literature
Machine Learning:
q C.M. Bishop.
Pattern Recognition and Machine Learning
2nd edition, Springer 2007.
q L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone.
Classification and Regression Trees
CRC Press reprint, 1998.
q N. Cristianini, J. Shawe-Taylor.
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cambridge University Press, 2000.
q T. Hastie, R. Tibshirani, J. Friedman.
The Elements of Statistical Learning
2nd edition, Springer, 2009.
q T. Mitchell.
Machine Learning
1st edition, McGraw-Hill, 1997. www.cs.cmu.edu/∼tom/mlbook.html
q V. Vapnik.
The Nature of Statistical Learning Theory
2nd edition, Springer 2000.
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Introduction
© STEIN/LETTMANN 2005-2015
Literature
Data Mining:
q D. Hand, H. Mannila, P. Smyth.
Principles of Data Mining
Bradford, 2001.
q P.N. Tan, M. Steinbach, V. Kumar.
Introduction to Data Mining
1st edition, Addison Wesley, 2005.
q I.H. Witten, E. Frank.
Data Mining: Practical Machine Learning Tools and Techniques
3rd edition, Morgan Kaufmann, 2011.
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Introduction
© STEIN/LETTMANN 2005-2015
Software
Programming:
q Eclipse Foundation, Inc., Canada.
Eclipse SDK
Version 4.5. www.eclipse.org/downloads
Machine Learning:
q E. Frank, M. Hall, G. Holmes, M. Mayo, B. Pfahringer, T. Smith, I. Witten.
Weka Machine Learning Project
Version 3.6. www.cs.waikato.ac.nz/ml/weka
q scikit-learn: Machine Learning in Python
Version 0.16 http://scikit-learn.org/stable/
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Introduction
© STEIN/LETTMANN 2005-2015
Software
Statistics:
q R Development Core Team.
R
Version 3.2. www.r-project.org
q E. Jones, T. Oliphant, P. Peterson and others.
SciPy
Version 0.16. www.scipy.org
q J.W. Eaton.
GNU Octave
Version 4.0. www.gnu.org/software/octave
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Introduction
© STEIN/LETTMANN 2005-2015